微软 AI 入门课

AI For Beginners：安装说明

这个工具包里只有安装说明，由 Mindket 编写。课程、课文、笔记本和实验都在微软的仓库里：https://github.com/microsoft/AI-For-Beginners

这些说明不是微软写的，也没有得到微软的认可。README 把这个仓库叫作“Artificial Intelligence for Beginners - A Curriculum”，并说：“Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI”，意思是：一套为期 12 周、共 24 节课的课程，有实践课、测验和实验，适合初学者，涵盖 TensorFlow 和 PyTorch 这类工具，也讲 AI 伦理。这些说明依据 2026 年 11 月 3 日读到的 README 和设置页面写成。

许可证：MIT。LICENSE 文件（https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE）写的是“Copyright (c) Microsoft Corporation.”。README 的 Special Thanks 部分把 Dmitry Soshnikov 博士列为主要作者，Jen Looper 博士列为编辑，Tomomi Imura 列为手绘笔记插画师。

这门课是什么

它是 Markdown 课文，加上 Jupyter 笔记本和实验。README 说每节课可以包含预读材料、可运行的 Jupyter 笔记本、部分主题的实验，以及指向 Microsoft Learn 模块的链接。笔记本“often specific to the framework (PyTorch or TensorFlow)”，也就是常常针对某个框架。笔记本里也有理论，所以 README 说至少要把其中一个版本完整过一遍。来源：https://github.com/microsoft/AI-For-Beginners/blob/main/README.md

README 说这门课不涵盖商业场景里的 AI、经典机器学习（它指向微软的 Machine Learning for Beginners 课程）、Cognitive Services、云端 ML 框架、对话式 AI 和聊天机器人，也不讲深度学习背后的深度数学。关于神经网络架构，它说：“We will cover recent models but may be a bit lacking in the state-of-the-art.”，意思是会讲较新的模型，但在最前沿上可能有所欠缺。

课程列表

标题抄自 README 的 Content 表格。文件夹在 lessons/ 下面。短横线表示表格里这一列没有列出内容。

| # | 课程 | 文件夹 | 表格里列出的笔记本 | 实验 |
|---|---|---|---|---|
| 0 | Course Setup | 0-course-setup | Setup Your Development Environment (how-to-run.md) | - |
| 01 | Introduction and History of AI | 1-Intro | - | - |
| 02 | Knowledge Representation and Expert Systems | 2-Symbolic | Expert Systems, Ontology, Concept Graph | - |
| 03 | Perceptron | 3-NeuralNetworks/03-Perceptron | 一个 | 有 |
| 04 | Multi-Layered Perceptron and Creating our own Framework | 3-NeuralNetworks/04-OwnFramework | 一个 | 有 |
| 05 | Intro to Frameworks (PyTorch/TensorFlow) and Overfitting | 3-NeuralNetworks/05-Frameworks | PyTorch, Keras, TensorFlow | 有 |
| 06 | Intro to Computer Vision. OpenCV | 4-ComputerVision/06-IntroCV | 一个 | 有 |
| 07 | Convolutional Neural Networks & CNN Architectures | 4-ComputerVision/07-ConvNets | PyTorch, TensorFlow | 有 |
| 08 | Pre-trained Networks and Transfer Learning and Training Tricks | 4-ComputerVision/08-TransferLearning | PyTorch, TensorFlow（TensorFlow 链接打开的是第 05 课的 IntroKerasTF.ipynb） | 有 |
| 09 | Autoencoders and VAEs | 4-ComputerVision/09-Autoencoders | PyTorch, TensorFlow | - |
| 10 | Generative Adversarial Networks & Artistic Style Transfer | 4-ComputerVision/10-GANs | PyTorch, TensorFlow | - |
| 11 | Object Detection | 4-ComputerVision/11-ObjectDetection | TensorFlow | 有 |
| 12 | Semantic Segmentation. U-Net | 4-ComputerVision/12-Segmentation | PyTorch, TensorFlow | - |
| 13 | Text Representation. Bow/TF-IDF | 5-NLP/13-TextRep | PyTorch, TensorFlow | - |
| 14 | Semantic word embeddings. Word2Vec and GloVe | 5-NLP/14-Embeddings | PyTorch, TensorFlow | - |
| 15 | Language Modeling. Training your own embeddings | 5-NLP/15-LanguageModeling | PyTorch, TensorFlow | 有 |
| 16 | Recurrent Neural Networks | 5-NLP/16-RNN | PyTorch, TensorFlow | - |
| 17 | Generative Recurrent Networks | 5-NLP/17-GenerativeNetworks | PyTorch, TensorFlow | 有 |
| 18 | Transformers. BERT. | 5-NLP/18-Transformers | PyTorch, TensorFlow | - |
| 19 | Named Entity Recognition | 5-NLP/19-NER | TensorFlow | 有 |
| 20 | Large Language Models, Prompt Programming and Few-Shot Tasks | 5-NLP/20-LangModels | PyTorch | - |
| 21 | Genetic Algorithms | 6-Other/21-GeneticAlgorithms | 一个 | - |
| 22 | Deep Reinforcement Learning | 6-Other/22-DeepRL | PyTorch, TensorFlow | 有 |
| 23 | Multi-Agent Systems | 6-Other/23-MultiagentSystems | - | - |
| 24 | AI Ethics and Responsible AI | 7-Ethics | Microsoft Learn: Responsible AI Principles（一个链接，没有笔记本） | - |
| 25 | Multi-Modal Networks, CLIP and VQGAN (Extras) | X-Extras/X1-MultiModal | 一个 | - |

README 把它们分成 Introduction to AI、Symbolic AI、Introduction to Neural Networks、Computer Vision、Natural Language Processing、Other AI Techniques、AI Ethics 和 Extras 几组。来源：https://github.com/microsoft/AI-For-Beginners/blob/main/README.md

几种运行方式

• setup/local-conda.md：你自己的电脑，用 Miniconda，再用 VS Code 或浏览器里的 Jupyter。
• setup/dev-container-and-codespaces.md：仓库的 .devcontainer 文件夹，以及 GitHub Codespaces。
• setup/binder-and-cloud-gpu.md：Binder，以及靠后课程可用的云端 GPU 方案。
• setup/beginner-examples.md：examples 文件夹里的四个小示例。
• setup/study-plan-and-quizzes.md：自学步骤、测验，以及用 Docsify 把课文当成本地网站来读。
• setup/for-teachers.md：For Educators 页面。

让克隆小一点

README 说仓库里有 50 多种语言的翻译，会“significantly increases the download size”，也就是明显增大下载体积。它给出了一个把翻译排除掉的稀疏检出做法。来源：https://github.com/microsoft/AI-For-Beginners/blob/main/README.md

git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

在 Windows CMD 里，README 给出的是：

git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"

更新或删除

读到的这些页面没有给出更新或卸载的步骤。这套课程是一个仓库，所以更新克隆就是用 Git 拉取，删除就是删掉文件夹。这两句是 Mindket 写的，不是微软写的。How to Run 页面确实给出了删除 conda 环境的做法：先 conda deactivate，再 conda env remove --name ai4beg。

同一个团队的其他课程

README 列出了这个团队的更多课程。其中 Machine Learning for Beginners 在 https://github.com/Microsoft/ML-for-Beginners ，Generative AI for Beginners 在 https://github.com/microsoft/generative-ai-for-beginners

来源

• README：https://github.com/microsoft/AI-For-Beginners/blob/main/README.md
• Getting Started with this Curricula：https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/setup.md
• How to Run the Code：https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/how-to-run.md
• For Educators：https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/for-teachers.md
• Beginner-Friendly AI Examples：https://github.com/microsoft/AI-For-Beginners/blob/main/examples/README.md
• Quizzes：https://github.com/microsoft/AI-For-Beginners/blob/main/etc/quiz-app/README.md
• environment.yml：https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml
• LICENSE：https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE